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08/09/2023
Understanding Auto-Scheduling Optimizations for Model Deployment via Visualizations
After completing the design and training phases, deploying a deep learni...
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08/07/2023
A Four-Pronged Defense Against Byzantine Attacks in Federated Learning
Federated learning (FL) is a nascent distributed learning paradigm to tr...
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07/15/2023
Why Does Little Robustness Help? Understanding Adversarial Transferability From Surrogate Training
Adversarial examples (AEs) for DNNs have been shown to be transferable: ...
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10/04/2022
Shielding Federated Learning: Mitigating Byzantine Attacks with Less Constraints
Federated learning is a newly emerging distributed learning framework th...
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05/16/2022
A scalable deep learning approach for solving high-dimensional dynamic optimal transport
The dynamic formulation of optimal transport has attracted growing inter...
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04/28/2022
Shielding Federated Learning: Robust Aggregation with Adaptive Client Selection
Federated learning (FL) enables multiple clients to collaboratively trai...
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12/29/2021
Challenges and approaches for mitigating byzantine attacks in federated learning
Recently emerged federated learning (FL) is an attractive distributed le...
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09/22/2019
